Method and system for non-inductively monitoring human health trend based on seat pressure

Abstract

The invention discloses a method and a system for non-inductive monitoring of human health trend based on seat pressure, a pressure bearing part of a seat is integrated with a pressure sensor, and the method comprises a calibration step, a non-inductive data acquisition and weight estimation step, a trend analysis modeling step and an intelligent early warning step. The seat forms are not limited, and the seat can be an intelligent cushion, an intelligent toilet lid, an office chair, a sofa, an automobile seat, an electric vehicle seat, a bicycle seat, a seat of fitness equipment or a wheelchair and the like. According to the invention, data acquisition and trend analysis are automatically completed in a non-inductive process when the user sits down naturally, continuous and passive monitoring and intelligent early warning of the health condition of the user are realized, and the problems of interruption, initiative and poor experience of a weight monitoring mode in a traditional method and system are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of intelligent health monitoring and digital medical technology, in particular to a method and system for monitoring human health trend based on chair pressure. BACKGROUND

[0002] Body weight is a key basic physiological parameter reflecting the health status of human body. Short-term abnormal fluctuations in body weight are often early signs of certain diseases (such as heart failure, edema caused by kidney disease); and long-term trends directly reflect the nutritional, metabolic and overall health status.

[0003] At present, the public mainly relies on traditional body scales or intelligent body scales to monitor body weight. Such devices have obvious limitations:

[0004] 1. Active measurement: users need to consciously and actively stand on the scale to measure, which is difficult to form continuous and habitual data records, and the data is isolated points, which is easy to miss key change nodes.

[0005] 2. Not friendly to certain groups of people: for the elderly, the disabled, patients in rehabilitation or obese people, it is difficult, inconvenient or even risky to complete the action of "standing on the scale", which makes them unable to effectively obtain this important health data.

[0006] 3. Limited data value: the value of single measurement result is limited, it lacks seamless integration with daily life, and it is difficult to provide continuous long-term trend analysis with clinical reference value.

[0007] Although smart home and wearable devices are developing rapidly, there is still a lack of an effective solution that can truly integrate into daily life and automatically complete high-frequency physiological parameter collection and trend analysis in a user-unaware state. Therefore, how to provide a system and method for monitoring human health trend in an unaware state has become an urgent goal for the industry to improve. SUMMARY

[0008] The technical problem to be solved by the present application is to provide a method and system for monitoring human health trend based on chair pressure in an unaware state, so that users can automatically complete data collection and trend analysis in the unaware process of natural sitting, realize continuous and passive monitoring and intelligent early warning of user health status, and overcome the problems of interrupted, active and poor experience in the traditional method and system of body weight monitoring.

[0009] To solve the above technical problems, the present application adopts the following technical solutions:

[0010] In a first aspect, the present application provides a method for monitoring human health trend based on chair pressure, wherein the pressure bearing part of the chair is integrated with a pressure sensor, and the method comprises:

[0011] Calibration step: obtaining a known accurate body weight value input by the user; obtaining the average pressure signal after the user sits on the seat in a habitual posture and the pressure signal is stable; establishing the reference body weight-pressure correspondence of the user;

[0012] Non-invasive data collection and weight estimation step: automatically collecting a stable pressure signal when the user sits down each time, and converting it into a weight estimation value according to the reference body weight-pressure correspondence;

[0013] Trend analysis modeling step: continuously recording the weight estimation value of each time, and constructing a personal health trend model of the user based on time series; the model is used to identify the long-term change trend and short-term abnormal fluctuation of the weight.

[0014] As a further improvement of the present application, the pressure bearing part includes but is not limited to the seat surface, the seat support leg, the contact point between the seat and the base, or the hinge point of the seat.

[0015] Further, it further includes a smart warning step, which is based on the identification result of the trend analysis modeling, and sends warning information to the user or a third party device when the identification result meets the preset warning condition.

[0016] Further, the preset warning condition is that the weight continues to rise by more than the first preset percentage value of the base value for a week; or the preset warning condition is that the weight increases sharply by a first preset weight value within 24 hours.

[0017] Further, the first preset percentage value is 2%; and the first preset weight value is 1.5 kg.

[0018] Further, the seat is a smart cushion, a smart toilet lid, an office chair, a sofa, a car seat, an electric vehicle seat, a bicycle seat, a seat of fitness equipment (such as a spin bike seat or a rowing machine seat), a wheelchair, etc.

[0019] In a second aspect, the present application also provides a system for monitoring human health trend based on non-invasive pressure of a seat, comprising:

[0020] A pressure sensor integrated in a pressure bearing part of the seat, for collecting pressure signals generated when the user sits down;

[0021] A calibration module for obtaining a known accurate body weight value input by the user; obtaining the average pressure signal after the user sits on the seat in a habitual posture and the pressure signal is stable; establishing the reference body weight-pressure correspondence of the user;

[0022] A non-invasive data collection and weight estimation module for automatically collecting a stable pressure signal when the user sits down each time, and converting it into a weight estimation value according to the reference body weight-pressure correspondence;

[0023] a trend analysis modeling module configured to continuously record the weight estimates of the user and construct a personal health trend model of the user based on the time series; the model is configured to identify long-term change trends and short-term abnormal fluctuations of the weight.

[0024] Further, the pressure bearing parts include, but are not limited to, below the seat surface, the support legs of the seat, the contact points of the seat with the base, or the hinge points of the seat.

[0025] Further, the system further comprises a smart early warning module configured to send early warning information to the user or a third party device based on the identification result of the trend analysis modeling.

[0026] Further, the seat is a smart seat cushion, a smart toilet cover, an office chair, a sofa, a car seat, an electric vehicle seat, a bicycle seat, a seat of fitness equipment (such as a spin bike seat or a rowing machine seat), or a wheelchair, etc.

[0027] A seat system comprising a seat, and further comprising the system for monitoring human health trends based on seat pressure sensing as described above, wherein the pressure sensor is integrated into the pressure bearing parts of the seat.

[0028] By adopting the technical scheme described above, the present application has at least the following beneficial effects:

[0029] 1. Unconscious and continuous monitoring: The present application fully integrates the monitoring into the daily sitting behavior of the user, realizes truly unconscious, high-frequency, and continuous data acquisition, and can capture subtle but important change trends that cannot be found by traditional methods.

[0030] 2. Universality and inclusiveness: The present application greatly reduces the requirement for user cooperation, is particularly suitable for the elderly, chronic disease patients, and rehabilitation patients, and provides unprecedented health monitoring accessibility.

[0031] 3. Maximization of data value: The present application changes from focusing on the “absolute value” of single measurement to focusing on the “relative change trend” of long-term, which has higher clinical reference value for health warning and chronic disease management.

[0032] 4. Platformization and high expansibility: The core principle of the present application does not depend on a specific seat form, and can be widely applied to smart home, smart elderly care, automotive electronics, sports and fitness, and medical rehabilitation, etc., to realize platformized technology output. BRIEF DESCRIPTION OF DRAWINGS

[0033] The above is only a summary of the technical scheme of the present application. In order to more clearly understand the technical means of the present application, the following will further describe the present application in detail in combination with the drawings and specific embodiments.

[0034] Figure 1 is a system block diagram based on the pressure of a seat to monitor the health trend of human body. DETAILED DESCRIPTION

[0035] Exemplary embodiments of the present application will be described in greater detail below with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it is understood that the present application can be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the application to those skilled in the art.

[0036] The present application provides a method and system for monitoring the health trend of human body based on the pressure of a seat. The main design concept of the present application is to integrate pressure sensors in the pressure bearing parts of the seat (such as under the seat surface, seat support legs, seat contact points with the base, or seat hinge points, etc.), combined with calibration, so that the user can automatically complete data collection and trend analysis in the process of natural sitting without feeling, and realize continuous, passive monitoring and intelligent early warning of the user's health status. The present application does not depend on a specific seat form, and can be widely applied in smart home, smart elderly care, automotive electronics, medical rehabilitation and other fields.

[0037] The method for monitoring the health trend of human body based on the pressure of a seat of the present embodiment comprises:

[0038] (1) Calibration step: obtaining the known accurate body weight value input by the user; obtaining the pressure signal mean value after the user sits on the seat with the habit sitting posture and the pressure signal is stable; establishing the reference body weight-pressure correspondence of the user.

[0039] When the user uses it for the first time, the user is guided to input the known accurate body weight value, and the user is guided to sit on the seat with the habit sitting posture, and after the pressure signal is stable, the pressure signal mean value is collected, and the reference body weight-pressure correspondence of the user is established. This method avoids the problem that the pressure and the body weight cannot be directly corresponded. The habit sitting posture can be the sitting posture with the user's feet on the ground.

[0040] (2) Unconscious data collection and weight estimation step: automatically collecting a stable pressure signal when the user sits down each time, and converting it into a body weight estimation value according to the reference body weight-pressure correspondence.

[0041] In the subsequent daily use of the user, when the user sits down, a stable pressure signal is automatically collected, and a body weight estimation value is converted according to the reference body weight-pressure correspondence. This method can completely integrate the monitoring into the user's daily sitting behavior, realize truly unconscious, high-frequency and continuous data collection, and capture subtle but important change trends that cannot be found by traditional methods.

[0042] (3) Trend analysis modeling step: continuously record the weight estimates of the past, and build a personal health trend model of the user based on time series; the model is used to identify long-term change trends and short-term abnormal fluctuations in weight.

[0043] By changing from focusing on the "absolute value" of a single measurement to focusing on the "relative change trend" of the long term and the short-term abnormal fluctuation, the latter has higher clinical reference value for health warning and chronic disease management.

[0044] (4) Intelligent warning step, based on the identification results of the trend analysis modeling, when the identification results meet the preset warning conditions, send warning information to the user or third-party device.

[0045] Among them, the preset condition can be selected according to the actual situation, preferably, two ways are adopted, such as the preset warning condition is that the continuous increase of more than the first preset percentage value of the base value for one week; or the preset warning condition is that the sharp increase of the first preset weight value within 24 hours. In this embodiment, the first preset percentage value is 2%; the first preset weight value is 1.5 kg, but it is not limited to the above specific numerical value, which can be adjusted appropriately according to the actual situation. In addition, the third-party device can be the device of the guardian.

[0046] Correspondingly, with the above Figure 1 The present application also provides a system for monitoring human health trend based on chair pressure, comprising:

[0047] A pressure sensor integrated in the pressure bearing part of the chair (such as under the seat surface, chair support legs, chair contact points with the base, or chair hinge points, etc.) for collecting pressure signals generated when the user sits down;

[0048] A calibration module for obtaining a known accurate weight value input by the user; obtaining the mean value of the pressure signal after the user sits on the chair in the usual sitting posture and the pressure signal is stable; establishing the reference weight-pressure correspondence of the user;

[0049] A non-invasive data acquisition and weight estimation module for automatically collecting a segment of stable pressure signal when the user sits down each time, and converting it into a weight estimate according to the reference weight-pressure correspondence;

[0050] A trend analysis modeling module for continuously recording the weight estimates of the past, and building a personal health trend model of the user based on time series; the model is used to identify long-term change trends and short-term abnormal fluctuations in weight.

[0051] The system further comprises an intelligent early warning module configured to perform early warning based on the recognition result of the trend analysis modeling, and send early warning information to a user or a third-party device when the recognition result meets preset early warning conditions.

[0052] As described above, the seat is not limited to a smart cushion, a smart toilet cover, an office chair, a sofa, a car seat, an electric vehicle seat, a bicycle seat, a seat of fitness equipment (such as a spinning bike seat or a rowing machine seat), a wheelchair, or the like.

[0053] In addition, the embodiment further provides a seat system comprising a seat and the system for monitoring human health trends based on seat pressure, wherein the pressure sensor is integrated into a pressure bearing part of the seat. The seat is a smart cushion, a smart toilet cover, an office chair, a sofa, a car seat, an electric vehicle seat, a bicycle seat, a seat of fitness equipment (such as a spinning bike seat or a rowing machine seat), a wheelchair, or the like. The pressure bearing part includes, but is not limited to, a seat surface, a seat support leg, a seat and base contact point, or a seat hinge point.

[0054] In summary, the present application automatically collects data and analyzes trends during the natural and unobtrusive process of sitting, continuously and passively monitors and intelligently warns the user of the health status, thereby overcoming the problems of interruption, active monitoring, and poor experience in the traditional weight monitoring method and system.

[0055] The above description is only a preferred embodiment of the present application, and does not limit the present application in any form. Those skilled in the art can make simple modifications, equivalent changes, or modifications to the above-described technical content, which are all within the scope of the present application.

Claims

1. A method for monitoring human health trends based on non-sensory seating pressure, characterized in that, The pressure bearing part of the seat is below the seat surface, the support leg of the seat, the contact point of the seat and the foundation, or the hinge point of the seat. The method further comprises a smart warning step, which is based on the identification result of the trend analysis modeling and gives a warning when the identification result meets a preset warning condition, and sends a warning message to the user or a third-party device. The preset warning condition is that the weight continuously increases by more than a first preset percentage value of the base value for one week; or the preset warning condition is that the weight sharply increases by a first preset weight value within 24 hours. The first preset percentage value is 2%, and the first preset weight value is 1.5 kg.

2. The method of claim 1, wherein, The seat is a smart seat cushion, a smart toilet cover, an office chair, a sofa, a car seat, an electric vehicle seat, a bicycle seat, a seat of fitness equipment, or a wheelchair.

3. The method of claim 1, wherein the health trend is determined based on a pressure of the seat. The method comprises:

4. The method of claim 3, wherein, a pressure sensor integrated in the pressure bearing part of the seat for collecting the pressure signal generated when the user sits down; 5. The method of claim 4, wherein, a calibration module for obtaining a known accurate body weight value input by the user, obtaining the average pressure signal after the user sits on the seat in a habitual sitting posture and the pressure signal is stable, and establishing a reference body weight-pressure correspondence of the user; 6. The method of claim 1-5, wherein, an unobtrusive data collection and weight estimation module for automatically collecting a stable pressure signal when the user sits down each time, and converting the weight estimation value according to the reference body weight-pressure correspondence; 7. A system for non-sensory monitoring of human health trends based on seating pressure, characterized in that, a trend analysis modeling module for continuously recording the weight estimation value each time, and constructing a personal health trend model of the user based on the time sequence; the model is used to identify the long-term change trend and short-term abnormal fluctuations of the weight. The pressure bearing part of the seat is below the seat surface, the support leg of the seat, the contact point of the seat and the foundation, or the hinge point of the seat. The method further comprises a smart warning module for giving a warning based on the identification result of the trend analysis modeling, and sending a warning message to the user or a third-party device when the identification result meets a preset warning condition. The seat is a smart seat cushion, a smart toilet cover, an office chair, a sofa, a car seat, an electric vehicle seat, a bicycle seat, a seat of fitness equipment, or a wheelchair. The method further comprises the system for monitoring human health trends based on the pressure of the seat according to any one of claims 7-10, and the pressure sensor is integrated in the pressure bearing part of the seat.

8. The system for monitoring health trends in a human being based on chair pressure without awareness according to claim 7, wherein, ​ 9. The system for monitoring health trends based on chair pressure sensing as claimed in claim 7 wherein, ​ 10. The system for monitoring health trends in a human being based on pressure on a seating furniture according to any of claims 7-9, characterized in that, ​ 11. A seating system comprising a seat, characterized in that ​

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